docs: def regional_probability_map(graph, xxx_todo_changeme):
      r"""
      Regional term based on a probability atlas.
      
      An implementation of a regional term, suitable to be used with the
      `~medpy.graphcut.generate.graph_from_voxels` function.
      
      Takes an image/graph/map as input where each entry contains a probability value for
      the corresponding GC graph node to belong to the foreground object. The probabilities
      must be in the range :math:`[0, 1]`. The reverse weights are assigned to the sink
      (which corresponds to the background).
      
      Parameters
      ----------
      graph : GCGraph
          The graph to add the weights to.
      probability_map : ndarray
          The label image.
      alpha : float
          The energy terms alpha value, balancing between boundary and regional term.
      
      Notes
      -----
      This function requires a probability atlas image of the same shape as the original image
      to be passed along. That means that `~medpy.graphcut.generate.graph_from_labels` has to
      be called with ``regional_term_args`` set to the probability atlas image.
      """
      (probability_map, alpha) = xxx_todo_changeme
      probability_map = scipy.asarray(probability_map)
      probabilities = numpy.vstack([(probability_map * alpha).flat,
                                    ((1 - probability_map) * alpha).flat]).T
      graph.set_tweights_all(probabilities)